MTEB Leaderboard

What is MTEB Leaderboard?

MTEB Leaderboard is your go-to tool for comparing and evaluating text embedding models—those nifty AI systems that convert words, phrases, or documents into numerical vectors for tasks like search, clustering, or recommendation engines. Whether you're a researcher fine-tuning a new model or a developer hunting for the best-performing embeddings, this leaderboard cuts through the noise by ranking models based on customizable benchmarks and language-specific performance. It’s like a report card for AI models, but way more interactive and way more useful.

Key Features

• Benchmark Customization: Pick from a buffet of evaluation tasks—think semantic similarity, text classification, or retrieval—to test models in scenarios that mirror your real-world use case.
• Multilingual Magic: Evaluate models across 100+ languages, making it perfect for global projects or cross-lingual research.
• Real-Time Leaderboard Updates: As new models drop, the rankings refresh automatically—no manual updates needed.
• Side-by-Side Comparisons: Drag-and-drop models to compare their strengths and weaknesses visually.
• Detailed Performance Breakdowns: Dive into metrics like accuracy, efficiency, or robustness with interactive charts.
• Open-Source Transparency: All benchmarks and evaluation code are public, so you can trust the results (or tweak them for your needs).
• Community-Driven: Crowdsourced benchmarks mean the leaderboard evolves with the latest AI breakthroughs.
• Use Case Filters: Narrow results to models optimized for specific tasks—like chatbots, code generation, or medical text analysis.

How to use MTEB Leaderboard?

1. Choose Your Battles: Select 2-3 benchmarks that align with your project—say, STS-B for semantic similarity or XNLI for cross-lingual understanding.
2. Language Lens: Filter results by language or region—perfect for finding models that handle Spanish dialects or low-resource languages.
3. Run the Evaluation: Hit "Compare" to let the tool fetch and score models based on your criteria.
4. Drill Down: Click any model to see granular performance metrics, training details, or paper links.
5. Share & Conquer: Export comparison charts to convince your team why Model X beats Model Y for your use case.
6. Stay Updated: Bookmark your favorite benchmarks to track how new models stack up over time.

Frequently Asked Questions

Why should I trust the leaderboard rankings?
The rankings are based on peer-reviewed benchmarks and open-source code, so you can replicate results or audit the methodology.

Does it support niche languages like Icelandic or Urdu?
Yes! The tool covers over 100 languages, including low-resource ones, thanks to community contributions.

Can I add my own model to the leaderboard?
Absolutely. Submit your model’s evaluation results via the open API, and it’ll appear alongside state-of-the-art systems.

How often are new benchmarks added?
The community updates benchmarks quarterly, but you can propose new tasks anytime through the GitHub repo.

What’s the deal with "multilingual" vs. "monolingual" evaluations?
Multilingual benchmarks test models across language pairs (e.g., English-French), while monolingual ones focus on single-language tasks.

How do I interpret the scores?
Higher scores mean better performance, but check the benchmark description—some metrics prioritize speed, others accuracy.

Can I use this for non-text AI models?
Not really. MTEB Leaderboard is laser-focused on text embeddings, so image or audio models won’t show up here.

Is there a mobile app?
Not yet, but the web interface is responsive, so you can geek out over model comparisons on your phone during your commute.